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Field
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computational tools to support the safe and ethical deployment of AI in clinical settings. The research focus is on AI performance monitoring, distribution shift detection, bias assessment, and stress testing
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. The project delves into areas such as hardware-based security measures, tamper detection, and the integration of explainable AI models within embedded platforms. Situated within the esteemed IVHM Centre and
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Language Model, or Artificial Intelligence be used? The impact of this research will be to enable practitioners and the stakeholders of systems models to make objective assessment of model qualities using
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distribution of normal cardiac anatomy and function (including motion) from healthy subjects. By establishing an understanding of "what normal looks like", these models will detect deviations from the norm and
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discover therapeutic targets relevant to Welsh populations. You’ll also help translate your computational insights into lab-based validation using experimental models, paving the way for new diagnostics and
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system using deep learning (DL). The project’s objectives include generating training data from synthetic datasets and real-world images (cadaver and actual intraoperative THR images), developing a marker
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interview system for university admissions. This project aims to improve the fairness, efficiency, and objectivity of student selection by reducing bias and subjectivity inherent in traditional interviews
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This PhD at Cranfield University explores the development of resilient, AI-enabled electronic systems capable of detecting faults and autonomously recovering from failures in real time. The project
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aims and objectives are met. Working closely with the supervisory team, develop and plan research objectives. Present information on research progress and outcomes to, for example, project lead, funding
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establishing an understanding of "what normal looks like", these models will detect deviations from the norm and effectively identify potential anomalies during testing. You will explore both self-supervised